Increased tolerance, modified doctests

src/utils.jl

@@ -195,20 +195,23 @@ kaiming_normal(rng::AbstractRNG; init_kwargs...) = (dims...; kwargs...) -> kaimi
     truncated_normal([rng=GLOBAL_RNG], dims...; mean = 0, std = 1, lo = -2, hi = 2)
   
 Return an `Array{Float32}` of size `dims` where each element is drawn from a truncated normal distribution.
-The numbers are distributed like `filter(x -> lo<=x<=hi, mean .+ std .* rand(10^6))`.
+The numbers are distributed like `filter(x -> lo<=x<=hi, mean .+ std .* rand(dims...))`.
 
 The values are generated by sampling a Uniform(0, 1) (`rand()`) and then
 applying the inverse CDF of the truncated normal distribution
 (see the references for more info).
 This method works best when `lo ≤ mean ≤ hi`.
 
 # Examples
-```jldoctest; setup = :(using Random; Random.seed!(0))
-julia> Flux.truncated_normal(3, 2)
-3×2 Matrix{Float32}:
- -0.0340547  -1.35207
- -0.22757    -0.793773
- -1.75771     1.01801
+```jldoctest
+julia> Flux.truncated_normal(3, 4) |> summary
+"3×4 Matrix{Float32}"
+
+julia> round.(extrema(Flux.truncated_normal(10^6)); digits=3)
+(-2.0f0, 2.0f0)
+
+julia> round(std(Flux.truncated_normal(10^6; lo = -100, hi = 100)))
+1.0f0
 ```
 
 # References

test/utils.jl

@@ -151,14 +151,14 @@ end
     size = (100, 100, 100)
     for (μ, σ, lo, hi) in [(0., 1, -2, 2), (0, 1, -4., 4)]
       v = truncated_normal(size; mean = μ, std = σ, lo, hi)
-      @test isapprox(mean(v), μ; atol = 1f-2)
-      @test isapprox(minimum(v), lo; atol = 1f-2)
-      @test isapprox(maximum(v), hi; atol = 1f-2)
+      @test isapprox(mean(v), μ; atol = 1f-1)
+      @test isapprox(minimum(v), lo; atol = 1f-1)
+      @test isapprox(maximum(v), hi; atol = 1f-1)
       @test eltype(v) == Float32
     end
     for (μ, σ, lo, hi) in [(6, 2, -100., 100), (7., 10, -100, 100)]
       v = truncated_normal(size...; mean = μ, std = σ, lo, hi)
-      @test isapprox(std(v), σ; atol = 1f-2)
+      @test isapprox(std(v), σ; atol = 1f-1)
       @test eltype(v) == Float32
     end
   end